Cognite Hybrid AI empowers data fusion by merging data-driven machine learning with physics-based modeling. It provides a solution for sensitive, complex production use cases and can be configured to run and schedule leading industry simulations. The system assists in generating synthetic data via connection to established physics simulators for virtual IIoT sensors. Automating workflows, deploying physics-guided machine learning and integrating physics simulators are steps towards enhanced operational productivity. Successful implementations have demonstrated potential annual revenue increase and net positive environmental impacts. It additionally aids in predicting and enhancing product quality.
- A blend of data-driven machine learning and physics-based modeling, providing an intelligent solution for complex production scenarios.
- Seamless integration with physics simulators hastens deployment of physics-guided machine learning at scale.
- Capabilities of synthesizing more data using domain insights from process simulations to unlock missing training data.
- Automated, collaborative workflows that foster seamless integration with third-party systems.
- Provides a centralized, contextualized data source for both hybrid AI models and subject experts, allowing for better decision-making processes.
Cognite Hybrid AI empowers data fusion by merging data-driven machine learning with physics-based modeling. It provides a solution for sensitive, complex production use cases and can be configured to run and schedule leading industry simulations. The system assists in generating synthetic data via connection to established physics simulators for virtual IIoT sensors. Automating workflows, deploying physics-guided machine learning and integrating physics simulators are steps towards enhanced operational productivity. Successful implementations have demonstrated potential annual revenue increase and net positive environmental impacts. It additionally aids in predicting and enhancing product quality.
- A blend of data-driven machine learning and physics-based modeling, providing an intelligent solution for complex production scenarios.
- Seamless integration with physics simulators hastens deployment of physics-guided machine learning at scale.
- Capabilities of synthesizing more data using domain insights from process simulations to unlock missing training data.
- Automated, collaborative workflows that foster seamless integration with third-party systems.
- Provides a centralized, contextualized data source for both hybrid AI models and subject experts, allowing for better decision-making processes.